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Data Scientist

Palo Alto  |  Full Time  |  Tech & Engineering

About Glide Health

Glide Health is the world’s first cross-functional revenue intelligence product for healthcare providers, which reduces denials, appeals costs and administrative waste by timely interventions delivered across the front office, treatment pathway planning and the back-office. The product delivers reduced cash collection cycles through intelligent denials management, improved patient engagement through affordable treatment pathways and increased revenues through reduced rejections.

Glide Health comes pre-built with deep domain knowledge about payers and managed care plans, which drives its highly sophisticated AI-based modeling of payer behavior customized to individual patients and their managed care plans. It preempts possible errors or gaps in claims by intervening in the front-office by ensuring requirements like prior authorizations are met. It digitally guides clinical care by offering customized pathway plan options that are more affordable for patients and optimizes revenue collections for the provider by integrating with practice management platforms. The patients’ liabilities and payer collectibles are modeled, while the patient is still with the provider long before the claims are processed in the back-office. This paves the way for better payment assurance from the patients and very accurate revenue projections for the provider. These early interventions significantly reduce the administrative costs of managing revenue cycle and shortens cash collection cycles through integrations with existing RCM platforms.

About the Role

The Data Scientist will drive the design and development of key artificial intelligence (AI) components of Glide Health data platform including rules intelligence engine, models for rule extraction, data structures for different rule categories and prediction models of payor behavior.

Responsibilities

As a Data Scientist of a fast-growing startup, the successful candidate will be leading the development and deployment of ML models. More specifically –

  • Data mining using state-of-the-art methods
  • Selecting features, building and optimizing classifiers using machine learning
    techniques
  • Extending the company’s data with third party sources of information when needed
  • Enhancing data collection procedures to include information that is relevant for
    building analytic systems.
  • Processing, cleansing, and verifying the integrity of data used for analysis
  • Creating automated anomaly detection systems and constant tracking of its performance
  • Creating new data models for storing, extending and sharing healthcare billing rules

About You

The successful candidate will have experience in working in innovative projects with fast-paced delivery schedules in startups or large enterprises:

  • Proven track record of analyzing large-scale complex data sets, modeling and machine learning algorithms
  • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
  • Experience with common data science toolkits, such as R, Python (NumPy, SciPy,
    Pandas ), Matlab
  • Experience in deep learning frameworks (e.g., Tensorflow, MxNet), and Large-scale
    optimization preferred.
  • Experience with NLP toolkits such as NLTK, OpenNLP, Stanford CoreNLP, etc.
  • Proficiency in using query languages such as SQL
  • Experience with NoSQL databases, such as MongoDB, Cassandra
  • Good applied statistics skills, such as distributions, statistical testing, regression, etc.
  • 5 – 10 years of experience in Applied Machine learning
  • Educational background in a relevant field (Computer Science, Applied Math,
    Statistics)